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In the framework of the European HearCom project, promising signal enhancement algorithms were developed and evaluated for future use in hearing instruments. To assess the algorithms' performance, five of the algorithms were selected and implemented on a common real-time hardware/software platform. Four test centers in Belgium, The Netherlands, Germany, and(More)
In the frame of the HearCom1 project five promising signal enhancement algorithms are validated for future use in hearing instrument devices. To assess the algorithm performance solely based on simulation experiments, a number of physical evaluation measures have been proposed that incorporate basic aspects of normal and impaired human hearing.(More)
The choice of the window function and window length in short time analysis-synthesis (AS) systems based on the discrete Fourier transform (DFT) has to balance conflicting requirements: Long windows provide high spectral resolution while short windows allow for high temporal resolution. Furthermore, for many applications a low algorithmic delay is desirable.(More)
A new block-based noise reduction system is proposed which focuses on the preservation of transient sounds like stops or speech onsets. The power level of consonants has been shown to be important for speech intelligibility. In single-channel noise reduction systems, however, these sounds are frequently severely attenuated. The main reasons for this are an(More)
We present a novel iterative method for the optimization of switchable pairs of window functions. These windows may be used for block-based spectral analysis-synthesis (AS) in low-delay speech enhancement systems, where the energy compaction of speech sounds is improved by switching the spectral AS windows. Optimization objectives of the approach take the(More)
Smoothing selected cepstral coefficients over time has been recently shown to be a powerful method in the enhancement of noisy speech signals. A difficulty that arises in this context is that averaging a random variable in the log-domain changes its mean in the linear domain. The knowledge of this bias is indispensable for most temporal cepstrum smoothing(More)
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